diff --git a/src/kedro_catalog/catalog.py b/src/kedro_catalog/catalog.py
index fc11b4e..375a4af 100644
--- a/src/kedro_catalog/catalog.py
+++ b/src/kedro_catalog/catalog.py
@@ -26,7 +26,7 @@ def save(self, name: str, data: t.Any) -> None: ...
 
 @dataclass
 class DataCatalog:
-    _datasets: dict[str, DatasetProtocol]
+    _dataset_configs: dict[str, DatasetConfig]
 
     @classmethod
     def from_config(cls, config: dict[str, dict[str, t.Any]]) -> DataCatalog:
@@ -34,14 +34,33 @@ def from_config(cls, config: dict[str, dict[str, t.Any]]) -> DataCatalog:
             name: DatasetConfig.model_validate(dataset_config)
             for name, dataset_config in config.items()
         }
-        datasets = {
-            name: find_dataset_class(dataset_config.type).from_spec(dataset_config.spec)
-            for name, dataset_config in dataset_configs.items()
-        }
-        return cls(datasets)
+
+        # This performs eager instantiation, which we want to defer!
+        # https://github.com/kedro-org/kedro/issues/2829
+        # datasets = {
+        #     name: find_dataset_class(dataset_config.type).from_spec(
+        #         dataset_config.specxy
+        #     )
+        #     for name, dataset_config in dataset_configs.items()
+        # }
+
+        # Alternatively, we could find all the dataset classes already,
+        # but that might trigger expensive imports
+        # dataset_proxys = {
+        #     name: (find_dataset_class(dataset_config.type), dataset_config.spec)
+        #     for name, dataset_config in dataset_configs.items()
+        # }
+
+        # We just store the validated configs instead
+        return cls(dataset_configs)
 
     def get_dataset(self, name: str) -> DatasetProtocol:
-        return self._datasets[name]
+        # NOTE: This could be a cache, maybe with @lru_cache
+        # but will that mean that datasets stay in memory for longer?
+        # Might need to use weakrefs or give up
+        dataset_config = self._dataset_configs[name]
+        ds_class, spec = find_dataset_class(dataset_config.type), dataset_config.spec
+        return ds_class.from_spec(spec)
 
     def load(self, name: str) -> t.Any:
         return self.get_dataset(name).load()